72 citations · 263 across the 31 of their papers we have counts for
4 papers · 1 filter
Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming
Haihao Lu, Jinwen Yang
There is a recent interest on first-order methods for linear programming (LP). In this paper,we propose a stochastic algorithm using variance reduction and restarts for solving sha…
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
David Applegate, Mateo Díaz, Oliver Hinder +4
We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications. In addit…
Faster First-Order Primal-Dual Methods for Linear Programming using Restarts and Sharpness
David Applegate, Oliver Hinder, Haihao Lu +1
First-order primal-dual methods are appealing for their low memory overhead, fast iterations, and effective parallelization. However, they are often slow at finding high accuracy s…
Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming
David Applegate, Mateo Díaz, Haihao Lu +1
We study the problem of detecting infeasibility of large-scale linear programming problems using the primal-dual hybrid gradient method (PDHG) of Chambolle and Pock (2011). The lit…